Descending-stair detection, approach, and traversal with an autonomous tracked vehicle

Abstract

This paper presents a strategy for descending-stair detection, approach, and traversal using inertial sensing and a monocular camera mounted on an autonomous tracked vehicle. At the core of our algorithm are vision modules that exploit texture energy, optical flow, and scene geometry (lines) in order to robustly detect descending stairwells during both far- and near-approaches. As the robot navigates down the stairs, it estimates its three-degrees-of-freedom (d.o.f.) attitude by fusing rotational velocity measurements from an on-board tri-axial gyroscope with line observations of the stair edges detected by its camera. We employ a centering controller, derived based on a linearized dynamical model of our system, in order to steer the robot along safe trajectories. A real-time implementation of the described algorithm was developed for an iRobot Packbot, and results from real-world experiments are presented.